{"id":"W4312102322","doi":"10.21203/rs.3.rs-2394107/v1","title":"Insights for precision healthcare from the 100,000 Genomes Cancer Programme","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Medical Research Council; Cambridge University Hospitals; University of Oxford; University College London; Imperial College London; National Institute for Health and Care Research; Oxford University Hospitals NHS Foundation Trust; Cancer Research UK; University of Cambridge; Department of Health and Social Care; Wellcome Trust","keywords":"Health care; Cancer; Genome; Biology; Computational biology; Political science; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02985691,0.0009847516,0.001275922,0.002480957,0.0009700513,0.0063902,0.002010284,0.006286901,0.01413134],"category_scores_gemma":[0.06261887,0.0004660657,0.001475681,0.002746596,0.002169132,0.003889018,0.007158564,0.008447344,0.002918869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005761672,"about_ca_system_score_gemma":0.01079637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01369758,"about_ca_topic_score_gemma":0.008965888,"domain_scores_codex":[0.9843695,0.00990387,0.0006773758,0.0009722824,0.00299197,0.001085098],"domain_scores_gemma":[0.932337,0.03756089,0.0031658,0.004786139,0.01146826,0.01068187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004111015,0.0001450615,0.009812374,0.001058699,0.0003220323,0.0003641312,0.0005785779,0.002890102,0.001437704,0.05661722,0.6214323,0.3049307],"study_design_scores_gemma":[0.0002170157,0.000397753,0.0136844,0.002679958,0.0001884207,0.0004578498,0.0007196266,0.00274617,0.001261549,0.1282665,0.8492203,0.0001604339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.006502846,0.05438369,0.02089036,0.8721086,0.008364587,0.0001539967,0.00920519,0.001492012,0.02689879],"genre_scores_gemma":[0.2459685,0.09123258,0.1697607,0.4306198,0.02384677,0.0009326252,0.02076248,0.001350603,0.01552584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02985691,"threshold_uncertainty_score":0.1579003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09146067540638506,"score_gpt":0.4193982542380128,"score_spread":0.3279375788316278,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}